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AIMachine LearningDatabaseQueriesLLMBeancountAutomation

MAC-SQL: Multi-Agent Collaborative Text-to-SQL

MAC-SQL (COLING 2025) uses three specialized agents — Selector for schema reduction, Decomposer for question decomposition, and Refiner for execution-guided SQL correction — to reach 59.59% execution accuracy on the BIRD benchmark; ablation shows the Refiner contributes the most (+4.63 points), with direct implications for Beancount ledger query generation.

AILLMDatabaseQueriesBeancountPlain-Text AccountingMachine Learning

DIN-SQL: Decomposed In-Context Learning for Text-to-SQL

DIN-SQL (NeurIPS 2023) decomposes text-to-SQL into schema linking, complexity classification, and SQL generation stages, lifting GPT-4 from 67.4% to 85.3% execution accuracy on Spider without fine-tuning — and the same decomposition strategy maps directly onto natural language interfaces for Beancount's BQL query language.

BeancountAILLMDatabaseQueriesMachine LearningPlain-Text Accounting

BIRD Benchmark: The Real-Database Gap in LLM Text-to-SQL

The BIRD benchmark (NeurIPS 2023) tests LLMs on 95 real databases — GPT-4 reaches only 54.89% execution accuracy with domain hints and 34.88% without, a 20-point gap that directly shapes what a natural-language BQL interface for Beancount would need to solve.